AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications
AI 摘要
AutoEG自动化利用第三方组件漏洞,提升黑盒Web应用渗透测试效率和成功率。
主要贡献
- 提出AutoEG,一个全自动多智能体漏洞利用框架。
- 设计两阶段攻击流程:漏洞触发逻辑提取和迭代优化利用。
- 在真实漏洞上验证了AutoEG的有效性,显著优于现有方法。
方法论
AutoEG首先提取漏洞触发逻辑,然后通过智能体与目标应用交互,根据反馈迭代优化漏洞利用过程。
原文摘要
Large-scale web applications are widely deployed with complex third-party components, inheriting security risks arising from component vulnerabilities. Security assessment is therefore required to determine whether such known vulnerabilities remain practically exploitable in real applications. Penetration testing is a widely adopted approach that validates exploitability by launching concrete attacks against known vulnerabilities in real-world black-box systems. However, existing approaches often fail to automatically generate reliable exploits, limiting their effectiveness in practical security assessment. This limitation mainly stems from two issues: (1) precisely triggering vulnerabilities with correct technical details, and (2) adapting exploits to diverse real-world deployment settings. In this paper, we propose AutoEG, a fully automated multi-agent framework for exploit generation targeting black-box web applications. AutoEG has two phases: First, AutoEG extracts precise vulnerability trigger logic from unstructured vulnerability information and encapsulates it into reusable trigger functions. Second, AutoEG uses trigger functions for concrete attack objectives and iteratively refines exploits through feedback-driven interaction with the target application. We evaluate AutoEG on 104 real-world vulnerabilities with 29 attack objectives, resulting in 660 exploitation tasks and 55,440 exploit attempts. AutoEG achieves an average success rate of 82.41%, substantially outperforming state-of-the-art baselines, whose best performance reaches only 32.88%.